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579 lines
20 KiB
Python
579 lines
20 KiB
Python
# Natural Language Toolkit: Probabilistic Chart Parsers
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#
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# Copyright (C) 2001-2020 NLTK Project
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# Author: Edward Loper <edloper@gmail.com>
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# Steven Bird <stevenbird1@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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Classes and interfaces for associating probabilities with tree
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structures that represent the internal organization of a text. The
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probabilistic parser module defines ``BottomUpProbabilisticChartParser``.
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``BottomUpProbabilisticChartParser`` is an abstract class that implements
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a bottom-up chart parser for ``PCFG`` grammars. It maintains a queue of edges,
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and adds them to the chart one at a time. The ordering of this queue
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is based on the probabilities associated with the edges, allowing the
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parser to expand more likely edges before less likely ones. Each
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subclass implements a different queue ordering, producing different
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search strategies. Currently the following subclasses are defined:
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- ``InsideChartParser`` searches edges in decreasing order of
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their trees' inside probabilities.
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- ``RandomChartParser`` searches edges in random order.
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- ``LongestChartParser`` searches edges in decreasing order of their
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location's length.
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The ``BottomUpProbabilisticChartParser`` constructor has an optional
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argument beam_size. If non-zero, this controls the size of the beam
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(aka the edge queue). This option is most useful with InsideChartParser.
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"""
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##//////////////////////////////////////////////////////
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## Bottom-Up PCFG Chart Parser
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##//////////////////////////////////////////////////////
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# [XX] This might not be implemented quite right -- it would be better
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# to associate probabilities with child pointer lists.
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import random
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from functools import reduce
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from nltk.tree import Tree, ProbabilisticTree
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from nltk.grammar import Nonterminal, PCFG
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from nltk.parse.api import ParserI
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from nltk.parse.chart import Chart, LeafEdge, TreeEdge, AbstractChartRule
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# Probabilistic edges
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class ProbabilisticLeafEdge(LeafEdge):
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def prob(self):
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return 1.0
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class ProbabilisticTreeEdge(TreeEdge):
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def __init__(self, prob, *args, **kwargs):
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TreeEdge.__init__(self, *args, **kwargs)
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self._prob = prob
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# two edges with different probabilities are not equal.
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self._comparison_key = (self._comparison_key, prob)
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def prob(self):
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return self._prob
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@staticmethod
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def from_production(production, index, p):
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return ProbabilisticTreeEdge(
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p, (index, index), production.lhs(), production.rhs(), 0
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)
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# Rules using probabilistic edges
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class ProbabilisticBottomUpInitRule(AbstractChartRule):
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NUM_EDGES = 0
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def apply(self, chart, grammar):
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for index in range(chart.num_leaves()):
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new_edge = ProbabilisticLeafEdge(chart.leaf(index), index)
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if chart.insert(new_edge, ()):
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yield new_edge
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class ProbabilisticBottomUpPredictRule(AbstractChartRule):
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NUM_EDGES = 1
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def apply(self, chart, grammar, edge):
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if edge.is_incomplete():
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return
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for prod in grammar.productions():
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if edge.lhs() == prod.rhs()[0]:
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new_edge = ProbabilisticTreeEdge.from_production(
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prod, edge.start(), prod.prob()
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)
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if chart.insert(new_edge, ()):
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yield new_edge
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class ProbabilisticFundamentalRule(AbstractChartRule):
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NUM_EDGES = 2
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def apply(self, chart, grammar, left_edge, right_edge):
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# Make sure the rule is applicable.
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if not (
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left_edge.end() == right_edge.start()
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and left_edge.nextsym() == right_edge.lhs()
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and left_edge.is_incomplete()
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and right_edge.is_complete()
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):
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return
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# Construct the new edge.
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p = left_edge.prob() * right_edge.prob()
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new_edge = ProbabilisticTreeEdge(
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p,
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span=(left_edge.start(), right_edge.end()),
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lhs=left_edge.lhs(),
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rhs=left_edge.rhs(),
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dot=left_edge.dot() + 1,
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)
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# Add it to the chart, with appropriate child pointers.
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changed_chart = False
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for cpl1 in chart.child_pointer_lists(left_edge):
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if chart.insert(new_edge, cpl1 + (right_edge,)):
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changed_chart = True
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# If we changed the chart, then generate the edge.
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if changed_chart:
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yield new_edge
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class SingleEdgeProbabilisticFundamentalRule(AbstractChartRule):
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NUM_EDGES = 1
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_fundamental_rule = ProbabilisticFundamentalRule()
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def apply(self, chart, grammar, edge1):
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fr = self._fundamental_rule
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if edge1.is_incomplete():
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# edge1 = left_edge; edge2 = right_edge
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for edge2 in chart.select(
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start=edge1.end(), is_complete=True, lhs=edge1.nextsym()
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):
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for new_edge in fr.apply(chart, grammar, edge1, edge2):
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yield new_edge
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else:
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# edge2 = left_edge; edge1 = right_edge
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for edge2 in chart.select(
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end=edge1.start(), is_complete=False, nextsym=edge1.lhs()
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):
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for new_edge in fr.apply(chart, grammar, edge2, edge1):
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yield new_edge
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def __str__(self):
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return "Fundamental Rule"
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class BottomUpProbabilisticChartParser(ParserI):
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"""
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An abstract bottom-up parser for ``PCFG`` grammars that uses a ``Chart`` to
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record partial results. ``BottomUpProbabilisticChartParser`` maintains
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a queue of edges that can be added to the chart. This queue is
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initialized with edges for each token in the text that is being
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parsed. ``BottomUpProbabilisticChartParser`` inserts these edges into
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the chart one at a time, starting with the most likely edges, and
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proceeding to less likely edges. For each edge that is added to
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the chart, it may become possible to insert additional edges into
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the chart; these are added to the queue. This process continues
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until enough complete parses have been generated, or until the
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queue is empty.
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The sorting order for the queue is not specified by
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``BottomUpProbabilisticChartParser``. Different sorting orders will
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result in different search strategies. The sorting order for the
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queue is defined by the method ``sort_queue``; subclasses are required
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to provide a definition for this method.
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:type _grammar: PCFG
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:ivar _grammar: The grammar used to parse sentences.
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:type _trace: int
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:ivar _trace: The level of tracing output that should be generated
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when parsing a text.
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"""
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def __init__(self, grammar, beam_size=0, trace=0):
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"""
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Create a new ``BottomUpProbabilisticChartParser``, that uses
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``grammar`` to parse texts.
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:type grammar: PCFG
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:param grammar: The grammar used to parse texts.
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:type beam_size: int
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:param beam_size: The maximum length for the parser's edge queue.
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:type trace: int
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:param trace: The level of tracing that should be used when
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parsing a text. ``0`` will generate no tracing output;
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and higher numbers will produce more verbose tracing
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output.
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"""
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if not isinstance(grammar, PCFG):
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raise ValueError("The grammar must be probabilistic PCFG")
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self._grammar = grammar
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self.beam_size = beam_size
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self._trace = trace
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def grammar(self):
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return self._grammar
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def trace(self, trace=2):
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"""
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Set the level of tracing output that should be generated when
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parsing a text.
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:type trace: int
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:param trace: The trace level. A trace level of ``0`` will
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generate no tracing output; and higher trace levels will
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produce more verbose tracing output.
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:rtype: None
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"""
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self._trace = trace
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# TODO: change this to conform more with the standard ChartParser
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def parse(self, tokens):
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self._grammar.check_coverage(tokens)
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chart = Chart(list(tokens))
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grammar = self._grammar
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# Chart parser rules.
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bu_init = ProbabilisticBottomUpInitRule()
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bu = ProbabilisticBottomUpPredictRule()
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fr = SingleEdgeProbabilisticFundamentalRule()
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# Our queue
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queue = []
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# Initialize the chart.
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for edge in bu_init.apply(chart, grammar):
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if self._trace > 1:
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print(
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" %-50s [%s]"
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% (chart.pretty_format_edge(edge, width=2), edge.prob())
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)
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queue.append(edge)
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while len(queue) > 0:
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# Re-sort the queue.
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self.sort_queue(queue, chart)
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# Prune the queue to the correct size if a beam was defined
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if self.beam_size:
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self._prune(queue, chart)
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# Get the best edge.
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edge = queue.pop()
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if self._trace > 0:
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print(
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" %-50s [%s]"
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% (chart.pretty_format_edge(edge, width=2), edge.prob())
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)
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# Apply BU & FR to it.
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queue.extend(bu.apply(chart, grammar, edge))
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queue.extend(fr.apply(chart, grammar, edge))
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# Get a list of complete parses.
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parses = list(chart.parses(grammar.start(), ProbabilisticTree))
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# Assign probabilities to the trees.
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prod_probs = {}
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for prod in grammar.productions():
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prod_probs[prod.lhs(), prod.rhs()] = prod.prob()
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for parse in parses:
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self._setprob(parse, prod_probs)
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# Sort by probability
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parses.sort(reverse=True, key=lambda tree: tree.prob())
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return iter(parses)
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def _setprob(self, tree, prod_probs):
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if tree.prob() is not None:
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return
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# Get the prob of the CFG production.
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lhs = Nonterminal(tree.label())
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rhs = []
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for child in tree:
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if isinstance(child, Tree):
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rhs.append(Nonterminal(child.label()))
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else:
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rhs.append(child)
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prob = prod_probs[lhs, tuple(rhs)]
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# Get the probs of children.
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for child in tree:
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if isinstance(child, Tree):
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self._setprob(child, prod_probs)
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prob *= child.prob()
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tree.set_prob(prob)
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def sort_queue(self, queue, chart):
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"""
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Sort the given queue of ``Edge`` objects, placing the edge that should
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be tried first at the beginning of the queue. This method
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will be called after each ``Edge`` is added to the queue.
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:param queue: The queue of ``Edge`` objects to sort. Each edge in
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this queue is an edge that could be added to the chart by
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the fundamental rule; but that has not yet been added.
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:type queue: list(Edge)
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:param chart: The chart being used to parse the text. This
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chart can be used to provide extra information for sorting
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the queue.
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:type chart: Chart
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:rtype: None
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"""
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raise NotImplementedError()
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def _prune(self, queue, chart):
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""" Discard items in the queue if the queue is longer than the beam."""
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if len(queue) > self.beam_size:
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split = len(queue) - self.beam_size
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if self._trace > 2:
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for edge in queue[:split]:
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print(" %-50s [DISCARDED]" % chart.pretty_format_edge(edge, 2))
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del queue[:split]
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class InsideChartParser(BottomUpProbabilisticChartParser):
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"""
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A bottom-up parser for ``PCFG`` grammars that tries edges in descending
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order of the inside probabilities of their trees. The "inside
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probability" of a tree is simply the
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probability of the entire tree, ignoring its context. In
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particular, the inside probability of a tree generated by
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production *p* with children *c[1], c[2], ..., c[n]* is
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*P(p)P(c[1])P(c[2])...P(c[n])*; and the inside
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probability of a token is 1 if it is present in the text, and 0 if
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it is absent.
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This sorting order results in a type of lowest-cost-first search
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strategy.
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"""
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# Inherit constructor.
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def sort_queue(self, queue, chart):
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"""
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Sort the given queue of edges, in descending order of the
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inside probabilities of the edges' trees.
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:param queue: The queue of ``Edge`` objects to sort. Each edge in
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this queue is an edge that could be added to the chart by
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the fundamental rule; but that has not yet been added.
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:type queue: list(Edge)
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:param chart: The chart being used to parse the text. This
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chart can be used to provide extra information for sorting
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the queue.
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:type chart: Chart
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:rtype: None
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"""
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queue.sort(key=lambda edge: edge.prob())
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# Eventually, this will become some sort of inside-outside parser:
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# class InsideOutsideParser(BottomUpProbabilisticChartParser):
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# def __init__(self, grammar, trace=0):
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# # Inherit docs.
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# BottomUpProbabilisticChartParser.__init__(self, grammar, trace)
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#
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# # Find the best path from S to each nonterminal
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# bestp = {}
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# for production in grammar.productions(): bestp[production.lhs()]=0
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# bestp[grammar.start()] = 1.0
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#
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# for i in range(len(grammar.productions())):
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# for production in grammar.productions():
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# lhs = production.lhs()
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# for elt in production.rhs():
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# bestp[elt] = max(bestp[lhs]*production.prob(),
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# bestp.get(elt,0))
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#
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# self._bestp = bestp
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# for (k,v) in self._bestp.items(): print(k,v)
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#
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# def _sortkey(self, edge):
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# return edge.structure()[PROB] * self._bestp[edge.lhs()]
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#
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# def sort_queue(self, queue, chart):
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# queue.sort(key=self._sortkey)
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class RandomChartParser(BottomUpProbabilisticChartParser):
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"""
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A bottom-up parser for ``PCFG`` grammars that tries edges in random order.
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This sorting order results in a random search strategy.
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"""
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# Inherit constructor
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def sort_queue(self, queue, chart):
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i = random.randint(0, len(queue) - 1)
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(queue[-1], queue[i]) = (queue[i], queue[-1])
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class UnsortedChartParser(BottomUpProbabilisticChartParser):
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"""
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A bottom-up parser for ``PCFG`` grammars that tries edges in whatever order.
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"""
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# Inherit constructor
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def sort_queue(self, queue, chart):
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return
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class LongestChartParser(BottomUpProbabilisticChartParser):
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"""
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A bottom-up parser for ``PCFG`` grammars that tries longer edges before
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shorter ones. This sorting order results in a type of best-first
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search strategy.
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"""
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# Inherit constructor
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def sort_queue(self, queue, chart):
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queue.sort(key=lambda edge: edge.length())
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##//////////////////////////////////////////////////////
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## Test Code
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##//////////////////////////////////////////////////////
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def demo(choice=None, draw_parses=None, print_parses=None):
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"""
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A demonstration of the probabilistic parsers. The user is
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prompted to select which demo to run, and how many parses should
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be found; and then each parser is run on the same demo, and a
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summary of the results are displayed.
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"""
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import sys, time
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from nltk import tokenize
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from nltk.parse import pchart
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# Define two demos. Each demo has a sentence and a grammar.
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toy_pcfg1 = PCFG.fromstring(
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"""
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S -> NP VP [1.0]
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NP -> Det N [0.5] | NP PP [0.25] | 'John' [0.1] | 'I' [0.15]
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Det -> 'the' [0.8] | 'my' [0.2]
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N -> 'man' [0.5] | 'telescope' [0.5]
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VP -> VP PP [0.1] | V NP [0.7] | V [0.2]
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V -> 'ate' [0.35] | 'saw' [0.65]
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PP -> P NP [1.0]
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P -> 'with' [0.61] | 'under' [0.39]
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"""
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)
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toy_pcfg2 = PCFG.fromstring(
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"""
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S -> NP VP [1.0]
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VP -> V NP [.59]
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VP -> V [.40]
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VP -> VP PP [.01]
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NP -> Det N [.41]
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NP -> Name [.28]
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NP -> NP PP [.31]
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PP -> P NP [1.0]
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V -> 'saw' [.21]
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V -> 'ate' [.51]
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V -> 'ran' [.28]
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N -> 'boy' [.11]
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N -> 'cookie' [.12]
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N -> 'table' [.13]
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N -> 'telescope' [.14]
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N -> 'hill' [.5]
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Name -> 'Jack' [.52]
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Name -> 'Bob' [.48]
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P -> 'with' [.61]
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P -> 'under' [.39]
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Det -> 'the' [.41]
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Det -> 'a' [.31]
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Det -> 'my' [.28]
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"""
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)
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demos = [
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("I saw John with my telescope", toy_pcfg1),
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("the boy saw Jack with Bob under the table with a telescope", toy_pcfg2),
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]
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if choice is None:
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# Ask the user which demo they want to use.
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print()
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for i in range(len(demos)):
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print("%3s: %s" % (i + 1, demos[i][0]))
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print(" %r" % demos[i][1])
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print()
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print("Which demo (%d-%d)? " % (1, len(demos)), end=" ")
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choice = int(sys.stdin.readline().strip()) - 1
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try:
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sent, grammar = demos[choice]
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except:
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print("Bad sentence number")
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return
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# Tokenize the sentence.
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tokens = sent.split()
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# Define a list of parsers. We'll use all parsers.
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parsers = [
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pchart.InsideChartParser(grammar),
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pchart.RandomChartParser(grammar),
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pchart.UnsortedChartParser(grammar),
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pchart.LongestChartParser(grammar),
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pchart.InsideChartParser(grammar, beam_size=len(tokens) + 1), # was BeamParser
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]
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# Run the parsers on the tokenized sentence.
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times = []
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average_p = []
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num_parses = []
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|
all_parses = {}
|
|
for parser in parsers:
|
|
print("\ns: %s\nparser: %s\ngrammar: %s" % (sent, parser, grammar))
|
|
parser.trace(3)
|
|
t = time.time()
|
|
parses = list(parser.parse(tokens))
|
|
times.append(time.time() - t)
|
|
p = reduce(lambda a, b: a + b.prob(), parses, 0) / len(parses) if parses else 0
|
|
average_p.append(p)
|
|
num_parses.append(len(parses))
|
|
for p in parses:
|
|
all_parses[p.freeze()] = 1
|
|
|
|
# Print some summary statistics
|
|
print()
|
|
print(" Parser Beam | Time (secs) # Parses Average P(parse)")
|
|
print("------------------------+------------------------------------------")
|
|
for i in range(len(parsers)):
|
|
print(
|
|
"%18s %4d |%11.4f%11d%19.14f"
|
|
% (
|
|
parsers[i].__class__.__name__,
|
|
parsers[i].beam_size,
|
|
times[i],
|
|
num_parses[i],
|
|
average_p[i],
|
|
)
|
|
)
|
|
parses = all_parses.keys()
|
|
if parses:
|
|
p = reduce(lambda a, b: a + b.prob(), parses, 0) / len(parses)
|
|
else:
|
|
p = 0
|
|
print("------------------------+------------------------------------------")
|
|
print("%18s |%11s%11d%19.14f" % ("(All Parses)", "n/a", len(parses), p))
|
|
|
|
if draw_parses is None:
|
|
# Ask the user if we should draw the parses.
|
|
print()
|
|
print("Draw parses (y/n)? ", end=" ")
|
|
draw_parses = sys.stdin.readline().strip().lower().startswith("y")
|
|
if draw_parses:
|
|
from nltk.draw.tree import draw_trees
|
|
|
|
print(" please wait...")
|
|
draw_trees(*parses)
|
|
|
|
if print_parses is None:
|
|
# Ask the user if we should print the parses.
|
|
print()
|
|
print("Print parses (y/n)? ", end=" ")
|
|
print_parses = sys.stdin.readline().strip().lower().startswith("y")
|
|
if print_parses:
|
|
for parse in parses:
|
|
print(parse)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
demo()
|